Real-Time Fraud Detection in Decentralized Cryptocurrency Networks using Hybrid CNN-LSTM Architecture

Abstract

Due to the increased levels of cryptocurrency transactions, novel frameworks are required to detect fraud in the real-time and make decentralized finance safe. It is suggested in this paper that SecureChainNet, which encompasses both CNNs and LSTM networks, would aid in the detection of fraud in cryptocurrency networks. The first phase involves the use of CNNs to discover difficult patterns within the structured transactions data that is largely undetected by traditional means. Subsequently, the transaction sequence is analyzed using LSTM module, which enhances the system to identify fraud patterns thus developing over time. When used together with blockchain, all the decisions made by a model are recorded into an immutable log, and the following auditing process reconfirms decentralized data verification and increased trust. Experimentally, SecureChainNet has been determined to possess a significantly higher degree of accuracy, precision, and recall compared to other methods when putting it to use with the real blockchain data. Additionally, the system has the capability of operating almost in real-time and so it can be used to trade cryptocurrencies being traded. The analysis notes that deep learning in conjunction with blockchain security has the potential of creating intelligent and dependable financial fraud systems.